English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In data streams, the data distribution of arriving observations at differenttime points may change - a phenomenon called concept drift. While detectingconcept drift is a relatively mature area of study, solutions to theuncertainty introduced by observations with missing values have only beenstudied in isolation. No one has yet explored whether or how these solutionsmight impact drift detection performance. We, however, believe that dataimputation methods may actually increase uncertainty in the data rather thanreducing it. We also conjecture that imputation can introduce bias into theprocess of estimating distribution changes during drift detection, which canmake it more difficult to train a learning model. Our idea is to focus onestimating the distance between observations rather than estimating the missingvalues, and to define membership functions that allocate observations tohistogram bins according to the estimation errors. Our solution comprises anovel masked distance learning (MDL) algorithm to reduce the cumulative errorscaused by iteratively estimating each missing value in an observation and afuzzy-weighted frequency (FWF) method for identifying discrepancies in the datadistribution. The concept drift detection algorithm proposed in this paper is asingular and unified algorithm that can handle missing values, but not animputation algorithm combined with a concept drift detection algorithm.Experiments on both synthetic and real-world data sets demonstrate theadvantages of this method and show its robustness in detecting drift in datawith missing values. These findings reveal that missing values exert a profoundimpact on concept drift detection, but using fuzzy set theory to modelobservations can produce more reliable results than imputation.

Concept Drift Detection: Dealing with MissingValues via Fuzzy Distance Estimations